Transfer learning is a machine learning technique where a model trained on one task is re-purposed or transferred to another related task. The idea is to leverage knowledge learned from one domain or task and apply it to a different but related domain or task, typically when the new task has limited labeled data available.

Here's how transfer learning generally works:

Pre-trained Model:

A pre-trained model is first trained on a large dataset for a specific task, such as image classification or natural language processing. This pre-training is often done on a large, generic dataset to learn general features and patterns.

Transfer Learning:

Instead of training a model from scratch for the target task, the pre-trained model is used as a starting point. The knowledge and features learned during pre-training are transferred or fine-tuned to the new task.

Fine-tuning:

The pre-trained model is further trained or fine-tuned on the new dataset specific to the target task. During fine-tuning, certain layers of the model may be frozen (kept unchanged) to preserve the learned features, while others are updated to adapt to the new task.